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Paraphrase Generation

Paraphrase Generation involves transforming a natural language sentence to a new sentence, that has the same semantic meaning but a different syntactic or lexical surface form.

Papers

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Showing 1–25 of 209 papers

TitleStatusHype
Enhancing Paraphrase Type Generation: The Impact of DPO and RLHF Evaluated with Human-Ranked DataCode0
A Large-Scale Benchmark for Vietnamese Sentence ParaphrasesCode0
SMAB: MAB based word Sensitivity Estimation Framework and its Applications in Adversarial Text GenerationCode0
Learning to Adapt to Low-Resource Paraphrase Generation—0
Latent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language ModelsCode0
Multi-Attribute Linguistic Tuning for Controlled Paraphrase Generation—0
Retrieve, Generate, Evaluate: A Case Study for Medical Paraphrases Generation with Small Language ModelsCode0
Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase Enrichment—0
Action Controlled ParaphrasingCode0
Parameter Efficient Diverse Paraphrase Generation Using Sequence-Level Knowledge Distillation—0
ParaFusion: A Large-Scale LLM-Driven English Paraphrase Dataset Infused with High-Quality Lexical and Syntactic Diversity—0
Improved Paraphrase Generation via Controllable Latent DiffusionCode0
SLPL SHROOM at SemEval2024 Task 06: A comprehensive study on models ability to detect hallucinationCode0
SemEval-2024 Shared Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes—0
Fine-tuning CLIP Text Encoders with Two-step Paraphrasing—0
Neural Machine Translation for Malayalam Paraphrase Generation—0
Vector-Quantized Prompt Learning for Paraphrase Generation—0
Paraphrase Types for Generation and DetectionCode1
A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase GenerationCode0
Contextual Data Augmentation for Task-Oriented Dialog Systems—0
Automatic and Human-AI Interactive Text Generation—0
Multilingual Lexical Simplification via Paraphrase GenerationCode0
Explicit Syntactic Guidance for Neural Text GenerationCode1
Emotion and Sentiment Guided Paraphrasing—0
ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-TranslationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HRQ-VAEiBLEU24.93—Unverified
2SeparatoriBLEU14.84—Unverified
#ModelMetricClaimedVerifiedStatus
1HRQ-VAEiBLEU18.42—Unverified
2SeparatoriBLEU5.84—Unverified
#ModelMetricClaimedVerifiedStatus
1HRQ-VAEBLEU27.9—Unverified